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96 results for “soil mapping”
Map of Soil Organic Carbon: Region of Murcia (Spain)
This data package contain four soil organic carbon (SOC) maps resulted from the best data-model agreement of the analysis carried out in the frame of the Ph.D. Thesis ‘MODELING ORGANIC CARBON FOR QUANTIFICATION OF RESERVOIRS IN TERRESTRIAL ECOSYSTEMS AT THE NATIONAL LEVEL’ (Pilar Durante). Theses maps correspond to the estimates of SOC concentration (SOCc, g/kg) and SOC stocks (SOCs, tC/ha), and their associated spatially explicit uncertainties maps, for the Region of Murcia at 0-30 cm and 100 m spatial resolution. To achieve this, we evaluated four different digital soil mapping (DSM) approaches to estimate SOCc and SOCs for the Region of Murcia (11,313 km2), a topographic and climatic complex area in southern Iberian Peninsula, at three spatial resolutions (100m, 250m, 1000m). Using a local SOC database (255 soil profiles), we founded that a Quantile Regression Forest (QRF) approach had the best data-model agreement at 100 m spatial resolution, with the best balance of accuracy, external validation, and interpretability. The QRF model showed a mean SOCc of 12.18 g/kg with an overall uncertainty of 10.54 g/kg and an accuracy percentage of 79%; meanwhile the mean SOCs was 27,572 GgC with an uncertainty of 0.016 GgC. The analysis showed that using local environmental covariates and local soil information to predict SOC within this region resulted in a relative improvement between ~40% (for SOCc) and ~65% (for SOCs) when compared with SOC products derived from national and global databases. Our results provided evidence that large discrepancy exists between national and global estimates for reporting SOC at a local scale. Consequently, local-to-regional efforts are needed to better describe SOC spatial variability to reduce uncertainty and improve the assessment of soil resources.
Map of soil organic carbon loss of mineral soils in Estonia
<p>The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales.</p> <p>The map was generated to evaluate soil organic carbon (SOC) loss in Estonian agricultural soils. It is directly related to SERENA project WP3, T3.2, D3.3 with the aim of applying cookbooks to assess soil threats or ecosystem services. This map is the outcome of applying a cookbook developed by ISRIC (Genova, G., Poggio, L., Kempen, B., & Colman, B. DSM Workflow Seedling. ISRIC - World Soil Information. https://doi.org/10.17027/ISRIC-FSX2-2691).</p> <p>The generated map of SOC loss expressed as absolute sequestration rate (t C ha-1 a-1) between 2015 and 2021 is in GEOTIFF format at the resolution of 100m. The input data for the cookbook was from the PANDA database, which contains regular soil monitoring and voluntary soil sampling data by farmers in Estonia. To achieve the aim for accounting SOC loss in agricultural soils temporal pairs were selected resulting in 1037 paired points where the interval between second sampling was more than 5 years. SOC stocks were calculated for the depth of 20 cm using the equation by Adams (1973) to calculate soil bulk density. The calculated SOC stock for time0 and time2 (> 5 years resampled locations) were used as input points for digital soil mapping, that is the ISRIC cookbook. </p>
EJPSOIL_SERENA: Maps of Soil Organic Carbon Loss Scenarios in Elva Parish, Estonia
<p>The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales. </p> <p>The study examined the effects of winter cropping systems on long-term soil fertility and their potential to mitigate SOC (Soil Organic Carbon) loss compared to bare soil during the winter months. It analyzed changes in SOC stocks (0–30 cm) at the field level in Elva Parish over the period 2020–2040, under different land-use scenarios. The modeling was based on a SOC stock map layer for Estonian mineral arable soils, developed by the Centre of Estonian Rural Research and Knowledge, which represented the baseline conditions in 2020. SOC stock projections were made using the RothC model, which simulates soil carbon turnover. </p> <p>In the first scenario (Scenario 1), the average SOC stock in Elva Parish by 2040 was estimated assuming the land would remain bare, without vegetation, during the winter months from October to April. In the second scenario (Scenario 2), the SOC stock projection accounted for the presence of winter vegetation, which means the soil is covered with vegetation year-round. The dataset includes four files: a projected SOC stock map for Elva Parish in 2040 and the stock changes from 2020–2040 under Scenario 1, along with a projected SOC stock map for 2040 and the stock changes from 2020–2040 under Scenario 2. </p>
Global map of soil bacterial richness
<p>This repository contains global model estimates of soil bacterial richness (Fig.4) as described in:</p> <p>Bickel, Samuel, Xi Chen, Andreas Papritz, and Dani Or. “A Hierarchy of Environmental Covariates Control the Global Biogeography of Soil Bacterial Richness.” <em>Scientific Reports</em> 9, no. 1 (August 20, 2019): 1–10. <a href="https://doi.org/10.1038/s41598-019-48571-w">https://doi.org/10.1038/s41598-019-48571-w</a>.</p>
Predicted soil organic carbon stock at 30 m in t/ha for 0-100 cm depth global / update of the map of mangrove forest soil carbon
<p>This is the 2nd update of maps produced by <a href="https://doi.org/10.1088/1748-9326/aabe1c">Sanderman et al (2018)</a>. The improvements to the <a href="https://opengeohub.github.io/spatial-prediction-eml/spatiotemporal-prediction-of-soil-organic-carbon.html">3D spatial predictions</a> include:</p> <ul> <li> <p>new updated global mangrove coverage map (contact Thomas Worthington),</p> </li> <li> <p>spatiotemporal predictions to account for differences in spectral reflectance at the time of field work,</p> </li> <li> <p>additional SOC points <a href="https://static-content.springer.com/esm/art%3A10.1038%2Fs41558-018-0162-5/MediaObjects/41558_2018_162_MOESM2_ESM.xlsx">published in Rovai et al. (2018)</a> used in model training (see gpkg file).</p> </li> </ul> <p>To open map in QGIS or similar, drag and drop the *.tif files. You can than add also the gpkg file contain the training points.</p> <p>Production steps (ensemble predictions using SuperLearner) are explained in detail at: </p> <ul> <li>R code: <a href="https://github.com/whrc/Mangrove-Soil-Carbon/">https://github.com/whrc/Mangrove-Soil-Carbon/</a> (see "R_code/GMW_mangroves_SOC_30m.R")</li> <li>Tutorial: <a href="https://envirometrix.github.io/PredictiveSoilMapping/soilmapping-using-mla.html#ensemble-predictions-using-superlearner-package">"Predictive Soil Mapping with R"</a></li> </ul> <p>Produced for the purpose of Mangrove Restoration Potential Map funded by The Nature Conservancy and IUCN. Contact TNC: Emily Landis <<a href="mailto:elandis@TNC.ORG">elandis@TNC.ORG</a>>. Contact IUCN / University of Cambridge: Thomas Worthington <<a href="mailto:taw52@cam.ac.uk">taw52@cam.ac.uk</a>>.</p> <ul> <li>The mangrove restoration potential map is available at: <a href="https://www.researchgate.net/deref/http%3A%2F%2Fmaps.oceanwealth.org%2Fmangrove-restoration%2F">http://maps.oceanwealth.org/mangrove-restoration/</a></li> </ul>
Ecological soil map NICHE Flanders - Ecologische NICHE bodemkaart Vlaanderen
<p>NL</p> <p><strong>ECOLOGISCHE NICHE BODEMKAART VLAANDEREN</strong></p> <p>De NICHE bodemkaart voor Vlaanderen is een ecologisch getinte vereenvoudigde bodemkaart die als input dient voor het ecohydrologisch model NICHE Vlaanderen (<a href="https://purews.inbo.be/ws/portalfiles/portal/5370206/Callebaut_etal_2007_NicheVlaanderen.pdf">Callebaut et al. 2007</a>). </p> <p>De bodem speelt een belangrijke rol voor standplaatscondities en het voorkomen van plantengemeenschappen. In NICHE Vlaanderen worden zogenaamde ‘ecologische bodemtypes’ gedefinieerd. De bodemkenmerken worden daarbij vereenvoudigd tot een paar ecologisch relevante kenmerken: de korrelgrootte en de aanwezigheid van organische stof, die bepalend zijn voor de vochtcondities, zuurgraad en het trofieniveau in de bodem.</p> <p>De NICHE bodemkaart onderscheidt 12 klassen:</p> <table> <tbody> <tr> <td>Cijfercode</td> <td>Lettercode</td> <td>Omschrijving</td> </tr> <tr> <td>2</td> <td>K1</td> <td>alluviale kleigronden, arm aan organisch materiaal</td> </tr> <tr> <td>3</td> <td>KV</td> <td>alluviale kleigronden, rijk aan organisch materiaal, venige klei, klei op veen</td> </tr> <tr> <td>5</td> <td>Le</td> <td>eolische leemgronden</td> </tr> <tr> <td>6</td> <td>MK</td> <td>maritieme klei</td> </tr> <tr> <td>7</td> <td>P</td> <td>trilveen</td> </tr> <tr> <td>8</td> <td>V</td> <td>veen</td> </tr> <tr> <td>11</td> <td>Z1</td> <td>humusarme zandgronden (dunne humuslaag), podzol</td> </tr> <tr> <td>12</td> <td>Z2</td> <td>humusrijke zandgronden (dikke humuslaag)</td> </tr> <tr> <td>13</td> <td>ZV</td> <td>venige zandgronden, moerige zandgronden, zandige veengronden</td> </tr> <tr> <td>14</td> <td>L1</td> <td>alluviale leemgronden, arm aan organisch materiaal</td> </tr> <tr> <td>15</td> <td>LV</td> <td>alluviale leemgronden, rijk aan organisch materiaal, venige leemgronden</td> </tr> <tr> <td>10</td> <td> </td> <td>gronden die niet in aanmerking komen voor NICHE Vlaanderen:</td> </tr> <tr> <td> </td> <td>NG</td> <td>niet gespecifieerd</td> </tr> <tr> <td> </td> <td>B</td> <td>bebouwde of sterk beïnvloede gronden</td> </tr> <tr> <td> </td> <td>D</td> <td>droge gronden</td> </tr> <tr> <td> </td> <td>W</td> <td>open water</td> </tr> </tbody> </table> <p>De NICHE bodemkaart is afgeleid van de digitale bodemkaart van Vlaanderen (Digitale versie van de Bodemkaart van Vlaanderen, uitgave 20/06/2017, Databank Ondergrond Vlaanderen). De stappen om een eenheid van de bodemkaart van Vlaanderen in een NICHE bodemtype om te zetten worden in detail beschreven in het NICHE rapport (Callebaut et al. 2007: hoofdstuk 3 pp 32-45 en bijlage 3.3).</p> <p>Als er terreingegevens beschikbaar zijn, kan er afgeweken worden van deze (NICHE) bodemkaart: hoofdstuk 3.4 van het NICHE rapport (Callebaut et al. 2007) licht toe hoe een NICHE bodemtype toegekend kan worden aan een bodemprofiel op basis van de textuur, de dikte en de opeenvolging van de verschillende horizonten.</p> <p><strong>Formaat</strong></p> <p>Vectoriële geografische informatie ter beschikking gesteld als:</p> <ul> <li>shapefile (.shp, .dbf, .shx) met projectiebeschrijving (.prj), ruimtelijke indexen (.sbn, . sbx), geospatial metadata in XML formaat (.shp.xml) en stijlen (ArcView Layer Format .lyr, Styled Layer Descriptor Format .sld)</li> <li>geopackage (.gpkg) met stijlen (Styled Layer Descriptor Format .sld)</li> </ul> <p>Geografische referentiesysteem: Belge 1972 / Belgian Lambert 72 (<a href="https://epsg.io/31370">EPSG-code 31370</a>)</p> <p>Hoogtereferentiesysteem: Tweede Algemene Waterpassing (<a href="https://www.ngi.be/website/tweede-algemene-waterpassing/">TAW</a>)</p> <p><strong>Attributen</strong></p> <ul> <li>fid- Id NICHE bodemkaart (geopackage)</li> <li>gid- Id digitale bodemkaart 2017</li> <li>Bodemtype- Bodemkaarteenheden volgens het Belgische bodemclassificatiesysteem (digitale bodemkaart 2017)</li> <li>Bodemser_c- Bodemserie bestaande uit 3 letters die staan voor textuur, drainage en profiel (digitale bodemkaart 2017)</li> <li>Bodemserie - Bodemserie: beschrijving voor textuur, drainage en profiel (digitale bodemkaart 2017)</li> <li>Unitype - Bodemkaarteenheden met zeepolders omgezet naar de bodemclassificatie van de rest van Vlaanderen (digitale bodemkaart 2017)</li> <li>Grove_leg - Gegeneraliseerde legende van de bodemkaart (digitale bodemkaart 2017)</li> <li>Substr_V_c - Substraten waarvan de lithologische aard verschilt van die van de oppervlakkige laag (lithologische discontinuiteit) (digitale bodemkaart 2017)</li> <li>Textuur_c - Grondsoort, aard van het moedermateriaal (digitale bodemkaart 2017)</li> <li>Drainage_c - Natuurlijke draineringsklasse, natuurlijke drainage (digitale bodemkaart 2017)</li> <li>Profontw_c - Profielontwikkeling (digitale bodemkaart 2017)</li> <li>Fase_c - Secundaire bodemkenmerken (digitale bodemkaart 2017)</li> <li>Varimoma_c - Variant van het moedermateriaal (digitale bodemkaart 2017)</li> <li>Variprof_c - Variant van de profielontwikkeling (digitale bodemkaart 2017)</li> <li>Streek - Landbouwstreek (digitale bodemkaart 2017)</li> <li>NICHE_let - Lettercode van het NICHE bodemtype</li> <li>NICHE_cijf - Cijfercode van het NICHE bodemtype</li> </ul> <p><strong>Wijzigingen sinds vorige versie</strong></p> <p>Sinds vorige versie (1.1):</p> <ul> <li>NICHE bodemkaart niet meer gebaseerd op versie 2001 van de bodemkaart, maar op versie 20/06/2017 incl. een update met enkele militaire domeinen (Beverlo, Kleine Brogel, Brasschaat) en met een unibodemtype voor de classificatie van de zeepolders, alsook een verbetering van verschillende fouten.</li> <li>namen van de velden veranderd</li> <li>cijfercode van de NICHE bodemtypes aangepast (2 t.e.m. 15 i.p.v. 20 000 t.e.m. 150 000)</li> <li>enkele verbeteringen van de vertaling tussen bodemtypes en NICHE bodemtypes: <ul> <li>sV, sV(o): ZV (i.p.v. V in v1.1)</li> <li>uvPep: LV (i.p.v. L1 in v1.1)</li> <li>GDa3x: LV (i.p.v. L1 in v1.1)</li> <li>v-Zdpb(z): Z1 (i.p.v. ZV in v1.1)</li> </ul> </li> </ul> <p><strong>Disclaimer</strong></p> <p>Deze kaart geeft de best beschikbare informatie maar is een vereenvoudiging van de werkelijkheid op terrein. Ten allen tijde geldt de reële situatie op terrein voor toepassing t.b.v. het beleidsmatig en wettelijk kader.</p> <p>EN</p> <p><strong>ECOLOGICAL SOIL MAP NICHE FLANDERS</strong></p> <p>The NICHE soil map for Flanders is an ecologically tinted simplified soil map that serves as input for the ecohydrological model NICHE Flanders (<a href="https://purews.inbo.be/ws/portalfiles/portal/5370206/Callebaut_etal_2007_NicheVlaanderen.pdf">Callebaut et al. 2007</a>).</p> <p>Soil plays an important role for habitat conditions and the occurrence of plant communities. In NICHE Flanders, so-called 'ecological soil types' are defined. The soil characteristics are thereby simplified to a few ecologically relevant characteristics: the grain size and the presence of organic matter, which determine the moisture conditions, acidity and the trophy level in the soil.</p> <p>The NICHE soil map distinguishes 12 classes:</p> <table> <tbody> <tr> <td>Numerical code</td> <td>Letter code</td> <td>Description</td> </tr> <tr> <td>2</td> <td>K1</td> <td>alluvial clay soils, poor in organic matter</td> </tr> <tr> <td>3</td> <td>KV</td> <td>alluvial clay soils, rich in organic matter, peaty clay, clay on peat</td> </tr> <tr> <td>5</td> <td>Le</td> <td>aeolian loamy soils</td> </tr> <tr> <td>6</td> <td>MK</td> <td>maritime clay</td> </tr> <tr> <td>7</td> <td>P</td> <td>quaking bog</td> </tr> <tr> <td>8</td> <td>V</td> <td>peat</td> </tr> <tr> <td>11</td> <td>Z1</td> <td>humus-poor sandy soils (thin humus layer), podzol</td> </tr> <tr> <td>12</td> <td>Z2</td> <td>humus-rich sandy soils (thick humus layer)</td> </tr> <tr> <td>13</td> <td>ZV</td> <td>peaty sandy soils, swampy sandy soils, sandy peat soils</td> </tr> <tr> <td>14</td> <td>L1</td> <td>alluvial loamy soils, poor in organic matter</td> </tr> <tr> <td>15</td> <td>LV</td> <td>alluvial loamy soils, rich in organic matter, peaty loams</td> </tr> <tr> <td>10</td> <td> </td> <td>grounds that do not qualify for NICHE Flanders:</td> </tr> <tr> <td> </td> <td>NG</td> <td>not specified</td> </tr> <tr> <td> </td> <td>B</td> <td>built-up or heavily influenced soils</td> </tr> <tr> <td> </td> <td>D</td> <td>dry soils</td> </tr> <tr> <td> </td> <td>W</td> <td>open water</td> </tr> </tbody> </table> <p>The NICHE soil map is derived from the digital soil map of Flanders (Digital version of the Soil map of Flanders, published on 20/06/2017, Database of the Subsoil in Flanders/Databank Ondergrond Vlaanderen). The steps to convert a unit of the soil map of Flanders into a NICHE soil type are described in detail in the NICHE report (Callebaut et al. 2007: chapter 3 pp 32-45 and appendix 3.3).</p> <p>If field data is available, it is possible to deviate from this (NICHE) soil map: chapter 3.4 of the NICHE report (Callebaut et al. 2007) explains how a NICHE soil type can be assigned to an observed soil profile based on the texture, the thickness and the succession of the different horizons.</p> <p><strong>Format</strong></p> <p>Vectorial geographic information provided as:</p> <ul> <li>shapefile (.shp, .dbf, .shx) with a description of the projection (.prj), spatial indexes (.sbn, .sbx), geospatial metadata in XML format (.shp.xml) and styles (ArcView Layer Format .lyr, Styled Layer Descriptor Format .sld)</li> <li>geopackage (.gpkg) with styles (Styled Layer Descriptor Format .sld)</li> </ul> <p>Geographical reference system: Belge 1972 / Belgian Lambert 72 (<a href="https://epsg.io/31370">EPSG code 31370</a>)</p> <p>Elevation Reference System: Second General Leveling (<a href="https://www.ngi.be/website/tweede-algemene-waterpassing/">Tweede Algemene Waterpassing - TAW</a>)</p> <p><strong>Attributes</strong></p> <ul> <li>fid - Id NICHE soil map (geopackage)</li> <li>gid - Id digital soil map 2017</li> <li>Bodemtype - Soil map units according to the Belgian soil classification system (digital soil map 2017)</li> <li>Bodemser_c - Core soil series: soil series consisting of 3 letters that stand for texture, drainage and profile (digital soil map 2017)</li> <li>Bodemserie - Core soil series: description for texture, drainage and profile (digital soil map 2017)</li> <li>Unitype - Soil map units with sea polders converted to the soil classification of the rest of Flanders (digital soil map 2017)</li> <li>Grove_leg - Generalized legend of the soil map (digital soil map 2017)</li> <li>Substr_V_c - Substrates whose lithological nature differs from that of the superficial layer (lithological discontinuity) (digital soil map 2017)</li> <li>Textuur_c - Soil type, nature of the parent material (digital soil map 2017)</li> <li>Drainage_c - Natural drainage class, natural drainage (digital soil map 2017)</li> <li>Profontw_c - Profile development (digital soil map 2017)</li> <li>Fase_c - Secondary soil features (digital soil map 2017)</li> <li>Varimoma_c - Variant of the parent material (digital soil map 2017)</li> <li>Variprof_c - Variant of the profile development (digital soil map 2017)</li> <li>Streek - Agricultural region (digital soil map 2017)</li> <li>NICHE_let - Letter code of the NICHE soil type</li> <li>NICHE_cijf - Numerical code of the NICHE soil type</li> </ul> <p><strong>Changes since previous version</strong></p> <p>Since previous version (1.1):</p> <ul> <li>NICHE soil map no longer based on version 2001 of the soil map, but on version 20/06/2017, including an update with some military domains (Beverlo, Kleine Brogel, Brasschaat) and with a uni-soil type for the classification of the sea polders, as well as the correction of various errors.</li> <li>names of the fields changed</li> <li>numerical code of the NICHE soil types changed (2-15 instead of 20 000-150 000)</li> <li>a few improvements to the translation between soil types and NICHE soil types: <ul> <li>sV, sV(o): ZV (instead of V in v1.1)</li> <li>uvPep: LV (instead of L1 in v1.1)</li> <li>GDa3x: LV (instead of L1 in v1.1)</li> <li>v-Zdpb(z): Z1 (instead of ZV in v1.1)</li> </ul> </li> </ul> <p><strong>Disclaimer</strong></p> <p>This map provides the best available information but is a simplification of the reality on the field. The real situation on the field prevails at all times over the NICHE soil map for all policy and legal applications.</p>
Textural soil maps, Colombia, 0 - 100 cm
These are the first texture maps of Colombia, obtained from national and global digital soil mapping products. The maps were developed at five standard depths (0-5, 5-15, 15-30, 30-60, and 60-100 cm) and standardized with Additive log-ratio (ALR) transformation. The maps were harmonized at 1 square km of spatial resolution. The data packages include the following set maps: texture maps obtained through the Ensemble Machine Learning (EML) algorithms called landmap and MACHISPLIN; texture maps obtained from SoilGrids platform; residual maps of the texture of the algorithms referenced above; and finally texture maps obtained through spatial ensemble technique.
Model outputs from the study "A scalable framework for soil property mapping tested across a highly diverse tropical data-scarce region"
<p>Model outputs from the study "A scalable framework for soil property mapping tested across a highly diverse tropical data-scarce region". The study is published as open access and can be found at the following link: <a href="https://www.sciencedirect.com/science/article/pii/S2950289625000326">https://www.sciencedirect.com/science/article/pii/S2950289625000326</a></p> <p> </p> <p>The file "SWAT_USERSOIL.csv" was included to facilitate the assimilation of the soil mapping data into the Soil & Water Assessment Tool (SWAT, https://swat.tamu.edu/) for hydrological modeling. </p> <p> </p> <p>Regarding the raster files, please note:</p> <p>a) All values in these datasets have been multiplied by 10,000 to optimize file sizes.</p> <p>b) Files are named using the variable acronym, followed by the corresponding soil layer. For outputs derived from pedotransfer functions (PTFs), the PTF reference is appended after the variable acronym.</p> <p>c) Available data decrease with increasing soil layer number. This occurs because not all locations (grid cells) have the same soil depth or number of soil layers.</p> <p> </p> <p>If you have any questions about the dataset or its use, please don't hesitate to contact us.</p> <p> </p> <p> </p>
Map data of historical global estimates of soil respiration
<p>The map data of global soil respiration converted to NetCDF format.</p><p>All open access available estimates were collated.</p><p>Shoji Hashimoto, Akihiko Ito, Kazuya Nishina (2023) "Divergent data-driven estimates of global soil respiration". Communications Earth & Environment, 4 Article number: 460</p><p><a href="https://doi.org/10.1038/s43247-023-01136-2 ">https://doi.org/10.1038/s43247-023-01136-2</a> </p><p>Refer to Table 1 for the study ID and data source or the attributions of the NetCDF file. </p>
PEATGRIDS: Mapping global peat thickness and carbon stock via digital soil mapping approach, dataset
<p>PEATGRIDS: a dataset containing the first peat thickness and carbon stock maps estimated over peatlands area across the globe at ~1 km x ~1 km resolution. Carbon stock was calculated across all depths of the predicted peat thickness, multiplied by peat bulk density (BD) and carbon content (CC) across five depths: 0-15 cm, 15-30 cm, 30-60 cm, 60-100 cm, and 100-200 cm. Mapping effort was performed using quantile random forest regression based on remotely sensed data and environmental covariates, including topography, climate, soil properties, and land cover. The maps cover areas potentially as peatlands according to the UNEP's global peatland map obtained from the <a title="Global Peat Database" href="https://greifswaldmoor.de/global-peatland-database-en.html" target="_blank" rel="noopener">Global Peat Database</a>. We may update this dataset in the future, please consider using the latest version. </p> <p>Note: This version (2.0.1) clarifies the metric units for carbon stock per area in the previous version (2.0). </p>
[[Deprecated]] DIGITAL SOIL TEXTURE MAPS OF ARGENTINA
<p>A new version has been uploaded by Guillermo Schulz.</p>
DIGITAL SOIL TEXTURE MAPS OF ARGENTINA
<p>Soil fractions of Argentina in g/100g, Clay, Silt and Sand, for 4 standard depth intervals (0–15, 15-30, 30–60, 60–100) at 1000 m resolution. Including textural classes for the four standard layers and error estimation using random forest.</p> <p>Global accuracy based on cross-validation</p> <table> <tbody> <tr> <td> <p><strong>sp</strong></p> </td> <td> <p><strong>RMSE</strong></p> </td> <td> <p><strong>Rsquared</strong></p> </td> <td> <p><strong>MAE</strong></p> </td> </tr> <tr> <td> <p><strong>Sand 0-15 cm</strong></p> </td> <td> <p><strong>16.189</strong></p> </td> <td> <p><strong>0.640</strong></p> </td> <td> <p><strong>11.069</strong></p> </td> </tr> <tr> <td> <p><strong>Sand 15-30 cm</strong></p> </td> <td> <p><strong>16.320</strong></p> </td> <td> <p><strong>0.629</strong></p> </td> <td> <p><strong>11.213</strong></p> </td> </tr> <tr> <td> <p><strong>Sand 30-60 cm</strong></p> </td> <td> <p><strong>16.676</strong></p> </td> <td> <p><strong>0.618</strong></p> </td> <td> <p><strong>11.364</strong></p> </td> </tr> <tr> <td> <p><strong>Sand 60-100 cm</strong></p> </td> <td> <p><strong>16.762</strong></p> </td> <td> <p><strong>0.587</strong></p> </td> <td> <p><strong>11.472</strong></p> </td> </tr> <tr> <td> <p><strong>silt 0-15 cm</strong></p> </td> <td> <p><strong>12.011</strong></p> </td> <td> <p><strong>0.638</strong></p> </td> <td> <p><strong>8.352</strong></p> </td> </tr> <tr> <td> <p><strong>silt 15-30 cm</strong></p> </td> <td> <p><strong>11.807</strong></p> </td> <td> <p><strong>0.608</strong></p> </td> <td> <p><strong>8.388</strong></p> </td> </tr> <tr> <td> <p><strong>silt 30-60 cm</strong></p> </td> <td> <p><strong>11.504</strong></p> </td> <td> <p><strong>0.561</strong></p> </td> <td> <p><strong>8.168</strong></p> </td> </tr> <tr> <td> <p><strong>silt 60-100 cm</strong></p> </td> <td> <p><strong>11.728</strong></p> </td> <td> <p><strong>0.583</strong></p> </td> <td> <p><strong>8.263</strong></p> </td> </tr> <tr> <td> <p><strong>clay 0-15 cm</strong></p> </td> <td> <p><strong>8.766</strong></p> </td> <td> <p><strong>0.475</strong></p> </td> <td> <p><strong>5.721</strong></p> </td> </tr> <tr> <td> <p><strong>clay 15-30 cm</strong></p> </td> <td> <p><strong>10.723</strong></p> </td> <td> <p><strong>0.452</strong></p> </td> <td> <p><strong>7.432</strong></p> </td> </tr> <tr> <td> <p><strong>clay 30-60 cm</strong></p> </td> <td> <p><strong>11.211</strong></p> </td> <td> <p><strong>0.557</strong></p> </td> <td> <p><strong>7.842</strong></p> </td> </tr> <tr> <td> <p><strong>clay 60-100 cm</strong></p> </td> <td> <p><strong>11.005</strong></p> </td> <td> <p><strong>0.536</strong></p> </td> <td> <p><strong>7.734</strong></p> </td> </tr> </tbody> </table> <p> </p> <p> </p>
Baseline map of 137Cs inventories in reference soil sites at the continental scales of South America
<p>This dataset contains the baseline map of <sup>137</sup>Cs inventories in reference soil sites (Bq m<sup>-2</sup>, decay-corrected to 2020) estimated by Partial Least Square Regression (PLSR) with a spatial resolution of 2 km at the continental scale of South America, as well as the prediction uncertainties of the baseline map (coefficient of variation, %).<br> Details information regarding this dataset can be found in the original publication:<br> Mapping the spatial distribution of global <sup>137</sup>Cs fallout in soils of South America as a baseline for Earth Science studies, Earth-Science Reviews, Volume 214, 2021, 103542, ISSN 0012-8252, https://doi.org/10.1016/j.earscirev.2021.103542.</p>
Fuzzy modelling and mapping soil moisture in Germany, link to research data and scientific software
<p>Research data and scientific software related to spatio-temporal estimations of ecological soil moisture with available data covering the whole territory of Germany and the Kellerwald National Park (Hesse). Temporal trends of modelled soil moisture for the time period 1961–2070 were statistically analyzed. Soil moisture changes (drying-out) at both national and regional levels were mapped.</p>
ArcGIS Map Packages and GIS Data for: A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, Gillreath-Brown et al. (2019)
<p><strong>ArcGIS Map Packages and GIS Data for Gillreath-Brown, Nagaoka, and Wolverton (2019)</strong></p> <p>**When using the GIS data included in these map packages, please cite all of the following:</p> <blockquote> <p>Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, 2019. PLoSONE 14(8):e0220457. <a href="http://doi.org/10.1371/journal.pone.0220457">http://doi.org/10.1371/journal.pone.0220457</a></p> <p>Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. ArcGIS Map Packages for: A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, Gillreath-Brown et al., 2019. Version 1. Zenodo. <a href="https://doi.org/10.5281/zenodo.2572018">https://doi.org/10.5281/zenodo.2572018</a></p> </blockquote> <p><strong>OVERVIEW OF CONTENTS</strong></p> <p>This repository contains map packages for Gillreath-Brown, Nagaoka, and Wolverton (2019), as well as the raw digital elevation model (DEM) and soils data, of which the analyses was based on. The map packages contain all GIS data associated with the analyses described and presented in the publication. The map packages were created in ArcGIS 10.2.2; however, the packages will work in recent versions of ArcGIS. (Note: I was able to open the packages in ArcGIS 10.6.1, when tested on February 17, 2019). The primary files contained in this repository are:</p> <ul> <li>Raw DEM and Soils data <ul> <li>Digital Elevation Model Data (Map services and data available from U.S. Geological Survey, National Geospatial Program, and can be downloaded from the <a href="https://viewer.nationalmap.gov/basic/">National Elevation Dataset</a>) <ul> <li><strong>DEM_Individual_Tiles</strong>: Individual DEM tiles prior to being merged (1/3 arc second) from USGS National Elevation Dataset.</li> <li><strong>DEMs_Merged</strong>: DEMs were combined into one layer. Individual watersheds (i.e., Goodman, Coffey, and Crow Canyon) were clipped from this combined DEM. </li> </ul> </li> <li> Soils Data (Map services and data available from <a href="https://data.nal.usda.gov/dataset/natural-resources-conservation-service-web-soil-survey">Natural Resources Conservation Service Web Soil Survey</a>, U.S. Department of Agriculture) <ul> <li><strong>Animas-Dolores_Area_Soils</strong>: Small portion of the soil mapunits cover the northeastern corner of the Coffey Watershed (CW).</li> <li><strong>Cortez_Area_Soils</strong>: Soils for Montezuma County, encompasses all of Goodman (GW) and Crow Canyon (CCW) watersheds, and a large portion of the Coffey watershed (CW).</li> </ul> </li> </ul> </li> <li>ArcGIS Map Packages <ul> <li><strong>Goodman_Watershed_Full_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the full Goodman Watershed (GW).</li> <li><strong>Goodman_Watershed_Mesa-Only_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the mesa-only Goodman Watershed.</li> <li><strong>Crow_Canyon_Watershed_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the Crow Canyon Watershed (CCW).</li> <li><strong>Coffey_Watershed_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the Coffey Watershed (CW).</li> </ul> </li> </ul> <p>For additional information on contents of the map packages, please see see "Map Packages Descriptions" or open a map package in ArcGIS and go to "properties" or "map document properties."</p> <p><strong>LICENSES</strong></p> <p>Code: <a href="http://opensource.org/licenses/MIT">MIT</a> year: 2019 <br> Copyright holders: Andrew Gillreath-Brown, Lisa Nagaoka, and Steve Wolverton</p> <p><strong>CONTACT</strong></p> <p><strong>Andrew Gillreath-Brown, PhD Candidate, RPA</strong><br> <a href="https://anthro.wsu.edu/">Department of Anthropology</a>, Washington State University<br> <a href="mailto:andrew.brown1234@gmail.com">andrew.brown1234@gmail.com</a> – Email<br> <a href="https://andrewgillreathbrown.wordpress.com/">andrewgillreathbrown.wordpress.com</a> – Web</p>
Redistribution of the shapefile of the digital soil map of the Flemish Region (status 2017-06-20)
<p>This is a redistribution of the shapefile of the '<a href="https://www.dov.vlaanderen.be/geonetwork/srv/dut/catalog.search#/metadata/5c129f2d-4498-4bc3-8860-01cb2d513f8f">Digitale bodemkaart van het Vlaams Gewest: bodemtypes, substraten, fasen en varianten van het moedermateriaal en de profielontwikkeling</a>' (digital soil map of the Flemish Region: soil types, substrates, phases, variants of the parent material, and profile development), originally published by 'Databank Ondergrond Vlaanderen’ (Subsurface Database of Flemish Region, DOV) under a CC-BY compatible license. Its shapefile has been redistributed as the <code>soilmap</code> data source, used in reproducible, analytical workflows on Flemish Natura 2000 habitats and regionally important biotopes. These workflows rely on a stable, clean (datafile-only) and uniform representation of each data source version, represented by a Zenodo DOI.</p> <p>The Belgian soil map was drawn up by intensive soil mapping from the 1950s to 1970s (Dudal et al., 2005). The map is based on the Belgian soil classification system. It is a national system that was set up exclusively for Belgian soils. The digital soil map of the Flemish Region is documented by Van Ranst & Sys (2000). Each spatial polygon, accurately digitized from the soil map sheets (published at map scale 1:20000) at scale 1:5000, includes information on soil types, substrates, phases, variants of the parent material and profile development. If available, the general characteristics and photos of a representative soil profile and environment can be obtained for each soil type. Finally, for each location it is possible to call in a scan of the analog soil map sheet, the corresponding explanation booklet and the basic maps on a 1: 5000 scale. The digital soil map was updated in 2017 with information from several military domains, a uniform soil type was generated for the polder area, and several mistakes were corrected.</p> <p>The data source is owned by ‘Vlaams Planbureau voor Omgeving’ (Flemish Planning Bureau for Environment, Department of Environment of the Flemish government).</p>
Soil type (World Reference Base) maps of Europe based on Ensemble Machine Learning and multiscale EO data
<h2><strong>Sub-dataset: WRB soil types probabilities (part 1)</strong></h2> <h2>Disclaimer</h2> <p>This is the first release of pan-EU predictions of soil health indicators (the Soil Health Data Cube). Use for testing purposes only. A publication describing methods used has been submitted to PeerJ and is in review. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commision. Neither the European Union nor the granting authority can be held responsible for them. The data is provided "as is". AI4SoilHealth project consortium and its suppliers and licensors hereby disclaim all warranties of any kind, express or implied, including, without limitation, the warranties of merchantability, fitness for a particular purpose and non-infringement. Neither AI4SoilHealth project Consortium nor its suppliers and licensors, makes any warranty that the Website will be error free or that access thereto will be continuous or uninterrupted. You understand that you download from, or otherwise obtain content or services through, the Website at your own discretion and risk.</p> <h2>Description</h2> <p>This dataset covers pan-European areas, including Ukraine, the UK, and Turkey. This data cube could be used for applications such as soil property mapping and comprehensive soil health assessment across Europe. The dataset spans four depth ranges and multiple time periods, providing information for studies on soil organic carbon stock and dynamics.</p> <p>This dataset is part of the "Soil type (World Reference Base) map of Europe based on Ensemble Machine Learning and multiscale EO data" dataset. Check the related identifiers section below to access other parts of the dataset.</p> <p>This data set includes:</p> <ul> <li><strong>Soil types classification and relative entropy:</strong><br> This data includes hard classes maps (185 soil type classes) produced by ensemble model and relative entropy (Kullback-Leibler divergence) maps in added information (bit) over a dummy distribution (scaled 1000x). </li> <li><strong>Soil types probabilities (part 1):</strong><br> This data includes 92 averaged probabilities (0-1) maps for classes from <strong>abruptic.acrisols</strong> to <strong>gleyic.arenosols</strong>. The probabilites were scaled 100x (0-100). </li> <li><strong>Soil types probabilities (part 2):</strong><br> This data includes 93 averaged probabilities maps for classes from <strong>gleyic.cambisols</strong> to <strong>vitric.andosols</strong>. The probabilites were scaled 100x (0-100). </li> </ul> <h3>Related identifiers</h3> <ul> <li><a href="https://zenodo.org/records/13838407">WRB soil types classification and relative entropy</a></li> <li><a href="https://zenodo.org/records/13837830">WRB soil types probabilities (part 1)</a></li> <li><a href="https://zenodo.org/records/13837832">WRB soil types probabilities (part 2)</a></li> </ul> <h3>Data Details</h3> <ul> <li><strong>Time period:</strong> long term.</li> <li><strong>Type of data:</strong> Soil types classification and model probabilities.</li> <li><strong>How the data was collected or derived:</strong> The data was derived using ensemble ML models.</li> <li><strong>Statistical methods used:</strong> Relative entropy (Kullback-Leibler divergence)</li> <li><strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Svalbard. </li> <li><strong>Coordinate reference system:</strong> EPSG:3035</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (900,000, 899,000, 7,401,000, 5,501,000)</li> <li><strong>Spatial resolution:</strong> 30m</li> <li><strong>Image size:</strong> 216,700P x 153,400L</li> <li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li> </ul> <h3>Support</h3> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc)</p> <h3>Name convention</h3> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. For example, in <strong>soil.types_ai4sh.ensemble_c_30m_s_20220101_20221231_epsg.3035_v20240917.tif</strong>, the fields are:</p> <ol> <li><strong>generic variable name:</strong> soil.types = soil types</li> <li><strong>variable procedure combination:</strong> ai4sh.ensemble.abruptic.acrisols = AI4SH project, ensemble model, abrupitc acrisols soil type.</li> <li><strong>Position in the probability distribution/variable type:</strong> m = mean | c = class | p = probability</li> <li><strong>Spatial support:</strong> 30m</li> <li><strong>Depth reference:</strong> s = surface</li> <li><strong>Time reference begin time:</strong> 20220101 = 2022-01-01</li> <li><strong>Time reference end time:</strong> 20221231 = 2022-12-31</li> <li><strong>Bounding box:</strong> eu = pan-Europe</li> <li><strong>EPSG code:</strong> epsg.3035</li> <li><strong>Version code:</strong> v20240917 = version from 2024-09-17</li> </ol>
Soil type (World Reference Base) maps of Europe based on Ensemble Machine Learning and multiscale EO data
<h2><strong>Sub-dataset: WRB soil types probabilities (part 2)</strong></h2> <h2>Disclaimer</h2> <p>This is the first release of pan-EU predictions of soil health indicators (the Soil Health Data Cube). Use for testing purposes only. A publication describing methods used has been submitted to PeerJ and is in review. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commision. Neither the European Union nor the granting authority can be held responsible for them. The data is provided "as is". AI4SoilHealth project consortium and its suppliers and licensors hereby disclaim all warranties of any kind, express or implied, including, without limitation, the warranties of merchantability, fitness for a particular purpose and non-infringement. Neither AI4SoilHealth project Consortium nor its suppliers and licensors, makes any warranty that the Website will be error free or that access thereto will be continuous or uninterrupted. You understand that you download from, or otherwise obtain content or services through, the Website at your own discretion and risk.</p> <h2>Description</h2> <p>This dataset covers pan-European areas, including Ukraine, the UK, and Turkey. This data cube could be used for applications such as soil property mapping and comprehensive soil health assessment across Europe. The dataset spans four depth ranges and multiple time periods, providing information for studies on soil organic carbon stock and dynamics.</p> <p>This dataset is part of the "Soil type (World Reference Base) map of Europe based on Ensemble Machine Learning and multiscale EO data" dataset. Check the related identifiers section below to access other parts of the dataset.</p> <p>This data set includes:</p> <ul> <li><strong>Soil types classification and relative entropy:</strong><br> This data includes hard classes maps (185 soil type classes) produced by ensemble model and relative entropy (Kullback-Leibler divergence) maps in added information (bit) over a dummy distribution (scaled 1000x). </li> <li><strong>Soil types probabilities (part 1):</strong><br> This data includes 92 averaged probabilities (0-1) maps for classes from <strong>abruptic.acrisols</strong> to <strong>gleyic.arenosols</strong>. The probabilites were scaled 100x (0-100). </li> <li><strong>Soil types probabilities (part 2):</strong><br> This data includes 93 averaged probabilities maps for classes from <strong>gleyic.cambisols</strong> to <strong>vitric.andosols</strong>. The probabilites were scaled 100x (0-100). </li> </ul> <h3>Related identifiers</h3> <ul> <li><a href="https://zenodo.org/records/13838407">WRB soil types classification and relative entropy</a></li> <li><a href="https://zenodo.org/records/13837830">WRB soil types probabilities (part 1)</a></li> <li><a href="https://zenodo.org/records/13837832">WRB soil types probabilities (part 2)</a></li> </ul> <h3>Data Details</h3> <ul> <li><strong>Time period:</strong> long term.</li> <li><strong>Type of data:</strong> Soil types classification and model probabilities.</li> <li><strong>How the data was collected or derived:</strong> The data was derived using ensemble ML models.</li> <li><strong>Statistical methods used:</strong> Relative entropy (Kullback-Leibler divergence)</li> <li><strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Svalbard. </li> <li><strong>Coordinate reference system:</strong> EPSG:3035</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (900,000, 899,000, 7,401,000, 5,501,000)</li> <li><strong>Spatial resolution:</strong> 30m</li> <li><strong>Image size:</strong> 216,700P x 153,400L</li> <li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li> </ul> <h3>Support</h3> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc)</p> <h3>Name convention</h3> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. For example, in <strong>soil.types_ai4sh.ensemble_c_30m_s_20220101_20221231_epsg.3035_v20240917.tif</strong>, the fields are:</p> <ol> <li><strong>generic variable name:</strong> soil.types = soil types</li> <li><strong>variable procedure combination:</strong> ai4sh.ensemble.abruptic.acrisols = AI4SH project, ensemble model, abrupitc acrisols soil type.</li> <li><strong>Position in the probability distribution/variable type:</strong> m = mean | c = class | p = probability</li> <li><strong>Spatial support:</strong> 30m</li> <li><strong>Depth reference:</strong> s = surface</li> <li><strong>Time reference begin time:</strong> 20220101 = 2022-01-01</li> <li><strong>Time reference end time:</strong> 20221231 = 2022-12-31</li> <li><strong>Bounding box:</strong> eu = pan-Europe</li> <li><strong>EPSG code:</strong> epsg.3035</li> <li><strong>Version code:</strong> v20240917 = version from 2024-09-17</li> </ol>
Soil type (World Reference Base) maps of Europe based on Ensemble Machine Learning and multiscale EO data
<h2><strong>Sub-dataset: WRB soil types classification and relative entropy</strong></h2> <h2>Disclaimer</h2> <p>This is the first release of pan-EU predictions of soil health indicators (the Soil Health Data Cube). Use for testing purposes only. A publication describing methods used has been submitted to PeerJ and is in review. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commision. Neither the European Union nor the granting authority can be held responsible for them. The data is provided "as is". AI4SoilHealth project consortium and its suppliers and licensors hereby disclaim all warranties of any kind, express or implied, including, without limitation, the warranties of merchantability, fitness for a particular purpose and non-infringement. Neither AI4SoilHealth project Consortium nor its suppliers and licensors, makes any warranty that the Website will be error free or that access thereto will be continuous or uninterrupted. You understand that you download from, or otherwise obtain content or services through, the Website at your own discretion and risk.</p> <h2>Description</h2> <p>This dataset covers pan-European areas, including Ukraine, the UK, and Turkey. This data cube could be used for applications such as soil property mapping and comprehensive soil health assessment across Europe. The dataset spans four depth ranges and multiple time periods, providing information for studies on soil organic carbon stock and dynamics.</p> <p>This dataset is part of the "Soil type (World Reference Base) map of Europe based on Ensemble Machine Learning and multiscale EO data" dataset. Check the related identifiers section below to access other parts of the dataset.</p> <p>This data set includes:</p> <ul> <li><strong>Soil types classification and relative entropy:</strong><br> This data includes hard classes maps (185 soil type classes) produced by ensemble model and relative entropy (Kullback-Leibler divergence) maps in added information (bit) over a dummy distribution (scaled 1000x). </li> <li><strong>Soil types probabilities (part 1):</strong><br> This data includes 92 averaged probabilities (0-1) maps for classes from <strong>abruptic.acrisols</strong> to <strong>gleyic.arenosols</strong>. The probabilites were scaled 100x (0-100). </li> <li><strong>Soil types probabilities (part 2):</strong><br> This data includes 93 averaged probabilities maps for classes from <strong>gleyic.cambisols</strong> to <strong>vitric.andosols</strong>. The probabilites were scaled 100x (0-100). </li> </ul> <h3>Related identifiers</h3> <ul> <li><a href="https://zenodo.org/records/13838407">WRB soil types classification and relative entropy</a></li> <li><a href="https://zenodo.org/records/13837830">WRB soil types probabilities (part 1)</a></li> <li><a href="https://zenodo.org/records/13837832">WRB soil types probabilities (part 2)</a></li> </ul> <h3>Data Details</h3> <ul> <li><strong>Time period:</strong> long term.</li> <li><strong>Type of data:</strong> Soil types classification and model probabilities.</li> <li><strong>How the data was collected or derived:</strong> The data was derived using ensemble ML models.</li> <li><strong>Statistical methods used:</strong> Relative entropy (Kullback-Leibler divergence)</li> <li><strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Svalbard. </li> <li><strong>Coordinate reference system:</strong> EPSG:3035</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (900,000, 899,000, 7,401,000, 5,501,000)</li> <li><strong>Spatial resolution:</strong> 30m</li> <li><strong>Image size:</strong> 216,700P x 153,400L</li> <li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li> </ul> <h3>Support</h3> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc)</p> <h3>Name convention</h3> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. For example, in <strong>soil.types_ai4sh.ensemble_c_30m_s_20220101_20221231_epsg.3035_v20240917.tif</strong>, the fields are:</p> <ol> <li><strong>generic variable name:</strong> soil.types = soil types</li> <li><strong>variable procedure combination:</strong> ai4sh.ensemble.abruptic.acrisols = AI4SH project, ensemble model, abrupitc acrisols soil type.</li> <li><strong>Position in the probability distribution/variable type:</strong> m = mean | c = class | p = probability</li> <li><strong>Spatial support:</strong> 30m</li> <li><strong>Depth reference:</strong> s = surface</li> <li><strong>Time reference begin time:</strong> 20220101 = 2022-01-01</li> <li><strong>Time reference end time:</strong> 20221231 = 2022-12-31</li> <li><strong>Bounding box:</strong> eu = pan-Europe</li> <li><strong>EPSG code:</strong> epsg.3035</li> <li><strong>Version code:</strong> v20240917 = version from 2024-09-17</li> </ol>
SERENA EJP Soil - Map of Soil Sealing of Italy
<p>The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales.</p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.